Application of Ant Colony Algorithm in Image Classification with Source Code Implementation

This program contains the source code for image classification applications based on the Ant Colony Optimization algorithm. The implementation leverages ACO's heuristic approach to achieve more accurate image classification results. The code is specifically designed to handle images with dimensions of 30x30 pixels. Ant Colony Optimization is a metaheuristic algorithm that simulates the behavior of ants in finding food sources and establishing optimal paths. Through pheromone trail deposition and evaporation mechanisms, the algorithm effectively explores solution spaces to find optimal classification boundaries. In this implementation, each "ant" represents a potential solution path through the feature space, with pheromone concentrations guiding the selection of optimal feature combinations for classification. The program incorporates key ACO components including path selection probability calculations, pheromone update rules, and evaporation mechanisms. Main functions handle image preprocessing, feature extraction, and the iterative optimization process where artificial ants construct solutions based on pheromone trails and heuristic information. The implementation includes parameters for controlling ant population size, evaporation rate, and iteration counts to balance exploration and exploitation. By simulating collective ant intelligence, this ACO-based approach demonstrates enhanced performance in image classification tasks, particularly in handling complex pattern recognition problems. The code structure includes modular components for easy parameter adjustment and performance evaluation, making it suitable for both research and practical applications in computer vision and machine learning domains.